TY - JOUR

T1 - Technical note

T2 - Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores

AU - Knoben, Wouter J M

AU - Freer, Jim E

AU - Woods, Ross

PY - 2019/10/25

Y1 - 2019/10/25

N2 - A traditional metric used in hydrology to summarize model performance is the Nash-Sutcliffe Efficiency (NSE). Increasingly an alternative metric, the Kling-Gupta Efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE = 1-√2 ≈ -0.41. Thus, KGE values greater than -0.41 indicate that a model improves upon the mean flow benchmark – even if the model’s KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad-hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.

AB - A traditional metric used in hydrology to summarize model performance is the Nash-Sutcliffe Efficiency (NSE). Increasingly an alternative metric, the Kling-Gupta Efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE = 1-√2 ≈ -0.41. Thus, KGE values greater than -0.41 indicate that a model improves upon the mean flow benchmark – even if the model’s KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad-hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.

U2 - 10.5194/hess-23-4323-2019

DO - 10.5194/hess-23-4323-2019

M3 - Article (Academic Journal)

VL - 23

SP - 4323

EP - 4331

JO - Hydrology and Earth System Sciences

JF - Hydrology and Earth System Sciences

SN - 1027-5606

ER -